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This course offers an applied introduction to the diverse statistical models designed to accommodate categorical dependent variables. We will cover the theoretical background of various models including logit/probit, multinomial, conditional and ordered logit as well as models for count data (poisson and negative binomial). We will also discuss hurdle and zero-inflated models. Using the statistics software R, we will have hands-on examples on how to implement these models, visualize effects and discuss model-of-fit measures. Prior knowledge of R is not necessary, but is certainly helpful. Students should have completed Statistik I and Statistik II. |
